Trajectory tracking control of unmanned aerial vehicle with disturbance observer and robust adaptive neural dynamic surface

{"This":[0],"paper":[1],"investigates":[2],"the":[3,105,122,130,133,142,152,181,186],"trajectory":[4],"tracking":[5,165],"control":[6,44,84,136,159],"problem":[7],"for":[8,62,86,154],"a":[9,27,114],"class":[10],"of":[11,69,124,144,157,185],"uncertain":[12],"strict":[13],"feedback":[14],"nonlinear":[15,49,56],"systems":[16],"subject":[17],"to":[18,30,65,103,120,140,172],"unknown":[19,70,106],"dynamics":[20],"and":[21,73,82,113,167,183],"time":[22],"varying":[23],"external":[24],"disturbances,":[25,72],"with":[26,48],"specific":[28],"application":[29],"unmanned":[31],"aerial":[32],"vehicle":[33],"(UAV)":[34],"longitudinal":[35],"motion.":[36],"A":[37],"novel":[38],"robust":[39],"adaptive":[40,174],"neural":[41,99,175],"dynamic":[42,134],"surface":[43,135],"(DSC)":[45],"scheme":[46],"integrated":[47],"disturbance":[50,57,91,168,178],"observers":[51,58],"(DOB)":[52],"is":[53,118,138],"proposed.":[54],"First,":[55],"are":[59,76,101],"systematically":[60],"constructed":[61],"each":[63],"subsystem":[64],"provide":[66],"real-time":[67],"estimates":[68,75],"bounded":[71],"these":[74],"explicitly":[77],"incorporated":[78],"into":[79],"both":[80],"virtual":[81,158],"actual":[83],"laws":[85],"active":[87],"compensation,":[88],"significantly":[89],"enhancing":[90],"rejection":[92,169],"capability.":[93],"Second,":[94],"radial":[95],"basis":[96],"function":[97],"(RBF)":[98],"networks":[100],"employed":[102],"approximate":[104],"continuous":[107],"functions":[108],"arising":[109],"from":[110],"system":[111],"dynamics,":[112],"parameter":[115],"aggregation":[116],"strategy":[117],"adopted":[119],"reduce":[121],"number":[123],"online":[125],"adaptation":[126],"parameters,":[127],"thereby":[128],"simplifying":[129],"implementation.":[131],"Third,":[132],"technique":[137],"utilized":[139],"overcome":[141],"\\"explosion":[143],"complexity\\"":[145],"inherent":[146],"in":[147],"conventional":[148,173],"backstepping":[149],"designs,":[150],"eliminating":[151],"need":[153],"analytical":[155],"differentiation":[156],"laws.":[160],"Numerical":[161],"simulations":[162],"demonstrate":[163],"superior":[164],"accuracy":[166],"capability":[170],"compared":[171],"DSC":[176],"without":[177],"observers,":[179],"validating":[180],"effectiveness":[182],"robustness":[184],"proposed":[187],"scheme.":[188]}

Authors

Institutions

Publication Details

Journal
PLoS ONE
Published
2026-09-18
DOI
https://doi.org/10.1371/journal.pone.0358433
Primary Topic
Adaptive Control of Nonlinear Systems
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

Trajectory tracking control of unmanned aerial vehicle with disturbance observer and robust adaptive neural dynamic surface

D. C. C. Wang, Youwu Liu, Xian Pan, Xiang Nan
PLoS ONE
Adaptive Control of Nonlinear Systems
article

Trajectory tracking control of unmanned aerial vehicle with disturbance observer and robust adaptive neural dynamic surface

D. C. C. Wang, Youwu Liu, Xian Pan, Xiang Nan
article en

Abstract

This paper investigates the trajectory tracking control problem for a class of uncertain strict feedback nonlinear systems subject to unknown dynamics and time varying external disturbances, with a specific application to unmanned aerial vehicle (UAV) longitudinal motion. A novel robust adaptive neural dynamic surface control (DSC) scheme integrated with nonlinear disturbance observers (DOB) is proposed. First, nonlinear disturbance observers are systematically constructed for each subsystem to provide real-time estimates of unknown bounded disturbances, and these estimates are explicitly incorporated into both virtual and actual control laws for active compensation, significantly enhancing disturbance rejection capability. Second, radial basis function (RBF) neural networks are employed to approximate the unknown continuous functions arising from system dynamics, and a parameter aggregation strategy is adopted to reduce the number of online adaptation parameters, thereby simplifying the implementation. Third, the dynamic surface control technique is utilized to overcome the "explosion of complexity" inherent in conventional backstepping designs, eliminating the need for analytical differentiation of virtual control laws. Numerical simulations demonstrate superior tracking accuracy and disturbance rejection capability compared to conventional adaptive neural DSC without disturbance observers, validating the effectiveness and robustness of the proposed scheme.

PLoS ONEVol. 21(9)
Henan Forestry Vocational College (CN), Sanming University (CN), College of Business and Technology (US)
Openalex Percentile: Top 15%
Adaptive Control of Nonlinear Systems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.